A Framework for Systematic Assessment of Clinical Trial Population Representativeness Using Electronic Health Records Data

Sun, Yingcheng; Butler, Alex; Diallo, I.; Kim, Jae Hyun; Ta, Casey; Rogers, James R.; Líu, Hao; Weng, Chunhua

doi:10.1055/s-0041-1733846

Cited by 4 publications

(5 citation statements)

References 26 publications

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“… James R. Rogers 2021 [ 32 ] USA To identify the extent of main clinical differences between clinical trial participants and nonparticipants using a combination of electronic health record and trial enrollment data. Yingcheng Sun 2021 [ 33 ] USA To systematically estimate the representativeness of the population in clinical trials using EHR data during the early design stage. Lindsay P. Zimmerman 2018 [ 34 ] USA To present a novel strategy for recruiting underrepresented, community-based participants for pragmatic research studies that leverage routinely collected EHR data.…”

Section: Resultsmentioning

confidence: 99%

“… Combining trial enrollment data with EHR data may be useful for better understanding of the generalizability of trial results. Yingcheng Sun 2021 [ 33 ] Using the EHR data to assess the clinical trials population representativeness, providing data-driven metrics to inform the selection and optimization of eligibility criteria. The EHRs data are useful for estimating the population representativeness of clinical trial study.…”

Section: Resultsmentioning

confidence: 99%

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Utilization of EHRs for clinical trials: a systematic review

Kalankesh,

Monaghesh

2024

BMC Med Res Methodol

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Background and objective Clinical trials are of high importance for medical progress. This study conducted a systematic review to identify the applications of EHRs in supporting and enhancing clinical trials. Materials and methods A systematic search of PubMed was conducted on 12/3/2023 to identify relevant studies on the use of EHRs in clinical trials. Studies were included if they (1) were full-text journal articles, (2) were written in English, (3) examined applications of EHR data to support clinical trial processes (e.g. recruitment, screening, data collection). A standardized form was used by two reviewers to extract data on: study design, EHR-enabled process(es), related outcomes, and limitations. Results Following full-text review, 19 studies met the predefined eligibility criteria and were included. Overall, included studies consistently demonstrated that EHR data integration improves clinical trial feasibility and efficiency in recruitment, screening, data collection, and trial design. Conclusions According to the results of the present study, the use of Electronic Health Records in conducting clinical trials is very helpful. Therefore, it is better for researchers to use EHR in their studies for easy access to more accurate and comprehensive data. EHRs collects all individual data, including demographic, clinical, diagnostic, and therapeutic data. Moreover, all data is available seamlessly in EHR. In future studies, it is better to consider the cost-effectiveness of using EHR in clinical trials.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Utilization of EHRs for clinical trials: a systematic review

Kalankesh,

Monaghesh

2024

BMC Med Res Methodol

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“…Fegeler et al 33 are currently developing a solution focusing on the administrative part including communication methods for performing virtual tumor boards. Studies in the field of recruitment support often tackle different aspects than searching fitting trials for a specific patients, for example, optimize eligibility criteria 34 or assess trial population representativeness, 35 supporting data collection 36 or helping to find patients for screening. 37,38 A key factor in enrollment of MTB patients in clinical trials are matching genomic alterations of the patient's tumor and the study's inclusion criteria, yet these alterations are not specified in a well-structured matter and often only found study's descriptive text.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Searching of Clinical Trials Made Easier in cBioPortal Using Patients' Genetic and Clinical Profiles

et al. 2022

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Background Molecular tumor boards (MTBs) cope with the complexity of an increased usage of genome sequencing data in cancer treatment. As for most of these patients, guideline-based therapy options are exhausted, finding matching clinical trials is crucial. This search process is often performed manually and therefore time consuming and complex due to the heterogeneous and challenging dataset. Objectives In this study, a prototype for a search tool was developed to demonstrate how cBioPortal as a clinical and genomic patient data source can be integrated with ClinicalTrials.gov, a database of clinical studies to simplify the search for trials based on genetic and clinical data of a patient. The design of this tool should rest on the specific needs of MTB participants and the architecture of the integration should be as lightweight as possible and should not require manual curation of trial data in advance with the goal of quickly and easily finding a matching study. Methods Based on a requirements analysis, interviewing MTB experts, a prototype was developed. It was further refined using a user-centered development process with multiple feedback loops. Finally, the usability of the application was evaluated with user interviews including the thinking-aloud protocol and the system usability scale (SUS) questionnaire. Results The integration of ClinicalTrials.gov in cBioPortal is achieved by a new tab in the patient view where the genomic profile for the search is prefilled and additional parameters can be adjusted. These parameters are then used to query the application programming interface (API) of ClinicalTrials.gov. The returned search results subsequently are ranked and presented to the user. The evaluation of the application resulted in an SUS score of 83.5. Conclusion This work demonstrates the integration of cBioPortal with ClinicalTrials.gov to use clinical and genomic patient data to search for appropriate trials within an MTB.

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“…The widespread adoption of electronic medical records (EMRs) in clinical research underscores the necessity of leveraging EMRs over traditional clinical trial data for predicting HF prognosis. 12 The complex and often nonlinear relationships between voluminous medical data and clinical outcomes pose a significant analytical challenge, diminishing the efficacy of linear models like logistic regression (Logit) for precise prediction. With the advent of artificial intelligence, machine learning (ML) has increasingly been applied to construct cardiovascular disease prediction models.…”

Section: Introductionmentioning

confidence: 99%

Interpretable Machine Learning Models Using Peripheral Immune Cells to Predict 90-Day Readmission or Mortality in Acute Heart Failure Patients

Chen,

Yang,

Han

et al. 2024

Clin Appl Thromb Hemost

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Background Acute heart failure (AHF) carries a grave prognosis, marked by high readmission and mortality rates within 90 days post-discharge. This underscores the urgent need for enhanced care transitions, early monitoring, and precise interventions for at-risk individuals during this critical period. Objective Our study aims to develop and validate an interpretable machine learning (ML) model that integrates peripheral immune cell data with conventional clinical markers. Our goal is to accurately predict 90-day readmission or mortality in patients AHF. Methods In our study, we conducted a retrospective analysis on 1210 AHF patients, segregating them into training and external validation cohorts. Patients were categorized based on their 90-day outcomes post-discharge into groups of ‘with readmission/mortality’ and ‘without readmission/mortality’. We developed various ML models using data from peripheral immune cells, traditional clinical indicators, or both, which were then internally validated. The feature importance of the most promising model was examined through the Shapley Additive Explanations (SHAP) method, culminating in external validation. Results In our cohort of 1210 patients, 28.4% (344) faced readmission or mortality within 90 days post-discharge. Our study pinpointed 10 significant indicators—spanning peripheral immune cells and traditional clinical metrics—that predict these outcomes, with the support vector machine (SVM) model showing superior performance. SHAP analysis further distilled these predictors to five key determinants, including three clinical indicators and two immune cell types, essential for assessing 90-day readmission or mortality risks. Conclusion Our analysis identified the SVM model, which merges traditional clinical indicators and peripheral immune cells, as the most effective for predicting 90-day readmission or mortality in AHF patients. This innovative approach promises to refine risk assessment and enable more targeted interventions for at-risk individuals through continuous improvement.

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A Framework for Systematic Assessment of Clinical Trial Population Representativeness Using Electronic Health Records Data

Cited by 4 publications

References 26 publications

Utilization of EHRs for clinical trials: a systematic review

Utilization of EHRs for clinical trials: a systematic review

Searching of Clinical Trials Made Easier in cBioPortal Using Patients' Genetic and Clinical Profiles

Interpretable Machine Learning Models Using Peripheral Immune Cells to Predict 90-Day Readmission or Mortality in Acute Heart Failure Patients

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